Journey Science for Better Experience - Kirk...
Transcript of Journey Science for Better Experience - Kirk...
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Kirk Borne @KirkDBorne
Principal Data Scientist
Booz Allen Hamilton
Journey Science for Better Experience (CX, UX, PX, DX, EX, …)
(Customer, User, Patient, Digital, Employee,…)
https://venturebeat.com/2015/12/29/these-5-marketing-tech-trends-will-be-huge-in-2016/ http://www.datasciencecentral.com/profiles/blogs/a-sneak-peek-at-the-future-of-artificial-intelligence-the-newes-1
Ever since we first explored our world…
http://www.livescience.com/27663-seven-seas.html
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…We have asked questions about everything around us.
https://atillakingthehun.wordpress.com/2014/08/07/atlantis-not-lost/
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So, we have collected evidence (data) to answer our questions,
which leads to more questions, which leads to more data collection,
which leads to more questions, which leads to BIG DATA!
y ~ 2 * x (linear growth)
y ~ 2 ^ x (exponential growth)
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https://www.linkedin.com/pulse/exponential-growth-isnt-cool-combinatorial-tor-bair
y ~ x! ≈ x ^ x → Combinatorial Growth! (all possible interconnections, linkages, and interactions)
Semantic, Meaning-filled Data:
• Ontologies (formal)
• Folksonomies (informal)
• Tagging / Annotation – Automated (Machine Learning)
– Crowdsourced
– “Breadcrumbs” (user trails)
Broad, Enriched Data:
• Linked Data (RDF)
– All of those combinations!
• Graph Databases
• Machine Learning
• Cognitive Analytics
• Context
• The 360o view
Making Sense of the World with Smart Data
The Human Connectome Project: mapping and linking the major pathways in the brain. http://www.humanconnectomeproject.org/
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“All the World is a Graph” – Shakespeare?
(Graphic by Cray, for Cray Graph Engine CGE)
http://www.cray.com/products/analytics/cray-graph-engine
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Simple Example of the Power of Graph: Semi-Metric Space
• Entity {1} is linked to Entity {2} (small distance A)
• Entity {2} is linked to Entity {3} (small distance B)
• Entity {1} is *not* linked directly to Entity {3} (Similarity Distance C = infinite)
• Similarity Distances between A, B, and C violate the triangle inequality!
{1} {3} {2}
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• Entity {1} is linked to Entity {2} (small distance A)
• Entity {2} is linked to Entity {3} (small distance B)
• Entity {1} is *not* linked directly to Entity {3} (Similarity Distance C = infinite)
• Similarity Distances between A, B, and C violate the triangle inequality!
• The connection between black hat entities {1} and {3} never appears explicitly in a
link network, or within a transactional database.
• Examples: (a) Customer Journey modeling; (b) Safety Incident Causal Factor
Analysis; (c) Medical Research Discoveries across disconnected journals, through
linked semantic assertions; (d) Marketing Attribution Analysis; (e) Fraud networks,
Illegal goods trafficking networks, Money-Laundering networks.
{1} {3} {2}
Simple Example of the Power of Graph: Semi-Metric Space
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360o View of the Aviation Customer Journey with Linked / Graph Data
… Omnichannel devices… … Travel reservation info…
… Context (Location, weather, and other contextual features)
… Demographics…
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… FAA Open Data…
Customer Journey Science by Clickfox.com (the model predicts Customer outcomes)
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Customer Journey Science by Clickfox.com (how it really looks to map the customer journey)
11 https://blog.clickfox.com/topic/wireless-telecom
Design Thinking and UX are at the heart of it! http://www.iotcentral.io/blog/user-experience-ux-is-at-the-heart-of-digital-transformation
Getting it Right – Learning from experience!
https://guycookson.com/2015/06/26/design-vs-user-experience/
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So, the aviation industry has a choice: The usual way of doing things, or using data-driven predictive science!
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https://www.linkedin.com/pulse/exponential-growth-isnt-cool-combinatorial-tor-bair
4 Types of Discovery from Data –
Using Algorithms that “learn from experience” 1) Class Discovery: Find the categories of objects
(population segments), events, and behaviors in your data. + Learn the rules that constrain the class boundaries (that uniquely distinguish them).
2) Correlation (Predictive and Prescriptive Power) Discovery: Find trends, patterns, and
dependencies in data, which reveal new governing principles or behavioral patterns (the “DNA”).
3) Novelty (Surprise!) Discovery: Find new,
rare, one-in-a-[million / billion / trillion] objects, events, and behaviors.
4) Association (or Link) Discovery: (Graph and
Network Analytics) – Find the unusual (interesting) co-occurring associations / links / connections.
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5 Levels of Analytics Maturity
in Data-Driven Applications 1) Descriptive Analytics
– Hindsight (What happened?)
2) Diagnostic Analytics
– Oversight (real-time / What is happening?
Why did it happen?)
3) Predictive Analytics
– Foresight (What will happen?)
4) Prescriptive Analytics
– Insight (How can we optimize what happens?)
(Follow the dots / connections in the graph!)
5) Cognitive Analytics – Right Sight (the 360 view , what is the right
question to ask for this set of data in this
context = Game of Jeopardy)
– Finds the right insight, the right action, the
right decision,… right now!
– Moves beyond simply providing answers, to
generating new questions and hypotheses.
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PREDICTIVE
Analytics
Find a function (i.e., the model) f(d,t) that
predicts the value of some predictive
variable y = f(d,t) at a future time t, given
the set of conditions found in the training
data {d}.
=> Given {d}, find y.
PRESCRIPTIVE
Analytics
Find the conditions {d’} that will produce a
prescribed (desired, optimum) value y at a
future time t, using the previously learned
conditional dependencies among the
variables in the predictive function f(d,t).
=> Given y, find {d’}.
Predictive vs Prescriptive: What’s the Difference?
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PREDICTIVE
Analytics
Find a function (i.e., the model) f(d,t) that
predicts the value of some predictive
variable y = f(d,t) at a future time t, given
the set of conditions found in the training
data {d}.
=> Given {d}, find y.
PRESCRIPTIVE
Analytics
Find the conditions {d’} that will produce a
prescribed (desired, optimum) value y at a
future time t, using the previously learned
conditional dependencies among the
variables in the predictive function f(d,t).
=> Given y, find {d’}.
Confucius says…
“Study your past to know
your future”
Predictive vs Prescriptive: What’s the Difference?
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PREDICTIVE
Analytics
Find a function (i.e., the model) f(d,t) that
predicts the value of some predictive
variable y = f(d,t) at a future time t, given
the set of conditions found in the training
data {d}.
=> Given {d}, find y.
PRESCRIPTIVE
Analytics
Find the conditions {d’} that will produce a
prescribed (desired, optimum) value y at a
future time t, using the previously learned
conditional dependencies among the
variables in the predictive function f(d,t).
=> Given y, find {d’}.
Confucius says…
“Study your past to know
your future”
Baseball philosopher Yogi Berra says…
“The future ain’t what it
used to be.”
Predictive vs Prescriptive: What’s the Difference?
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Context is King! “You can see a lot just by looking.” – Yogi Berra
• Context is “other data” about your data = i.e., Metadata!
• The 3 most important things in your data are: Metadata, Metadata,
Metadata!
• Metadata are…
– Other Data that describes Other Data
– Other Data that describes Your Data
– Your Data that describes Other Data
• e.g., Connected “Smart” Cars = that car that is braking 3 vehicles
ahead of you = informs your vehicle to brake now!
• The Smart Enterprise = predictive / prescriptive maintenance algorithm
alerts the corresponding asset, the right skilled technician, & the right
tool to converge at right place at the right time!
• Contextual data empowers both Prescriptive and Cognitive Analytics.
• Open Data + IoT sensor data provide a lot of context data (metadata!) 19
• The 7 V’s of Open Data: • VALIDITY (data quality, usability)
• VISIBILITY (exposing your data’s issues)
• VARIETY (heterogeneous types, formats)
• VOICE (to all stakeholders)
• VOCABULARY (data models, semantics)
• VULNERABILITY (open to everyone!)
• proVenance (lineage, chain of custody)
• The most important V is: • VALUE (innovation, insight creation)
Big Value from (Big) Open Data • The 3 V’s of Big Data:
• VOLUME
• VARIETY
• VELOCITY
• The most important V is: • VALUE
20 http://rocketdatascience.org/?p=410
The Journey Science Roadmap for Better Experience
• Design Patterns for Streaming Data Analytics: – Detecting POI (Person of Interest, or Pattern of Interest, or any Point of Interest)
– Detecting BOI (Behavior of Interest from any “dynamic actor”)
– Precomputed scenarios and their responses (to speed up “next best action”)
– Design Thinking : DX, UX, CX, EX (Digital / User / Customer / Employee eXperience)
• Edge Analytics (what else is happening now at the point of data collection?)
– Locality in Time
• Near-field Analytics (who / what is local to this person / place / thing?)
– Locality in Geospace
• Related-entity Analytics (what else is similar to this entity / event?)
– Locality in Feature Space
• Agile Analytics: DataOps, Fail-fast, Iterative, MVP, Learning Systems
Minimum Viable Product = your POV (Proof of Value)
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@KirkDBorne
@BoozDataScience
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